A financial institution or credit card company. Case Study: Graph-based Techniques in Action The Client The Challenge
The deployment of a real-time fraud detection system capable of accurately identifying fraudulent transactions. This proactive approach enables the financial institution to initiate timely countermeasures, mitigating risks. The client is grappling with detecting and preventing fraudulent transactions within their credit card platform. Their goals are twofold — to curtail financial losses and to shield their customers from unauthorized charges.
The Proposed Solution
To illuminate complex transactional relationships, which are instrumental in detecting potential fraudulent behavior.
Fractal's Role
Our approach follows a logical progression from extracting the relevant data to evaluating the results.
Handling null values, handling categorical values, dropping off unnecessary features
Tabular Dataset to Graph Networks using networkx library
Finding edge weight distribution, node degree distribution, centralities etc.
Credit Card dataset
Data Extraction
Data pre-processing
Graph Network
Exploratory Data Analysis
Evaluating the performance of the model
Model training using classifiers & train - te st split using stratified k-fold
Handling categorical values using one hot encoding, standardizing the features
Evaluation
Model Building
Final Data Preparation
To accurately reflect the connections between customers and their transactions, Fractal creates graphs that are structured as follows:
Nodes: These represent the credit card number and merchant. Edges denote transactions between the credit card number and the merchant. Edge Weight: This signifies the transaction's magnitude or amount.
When graph features are incorporated into the model, they emerge as the most influential factors. In our case study, an increase in accuracy was noted, with the Area Under the Curve (AUC) metric rising from 0.72 to 0.76, reflecting an increase of nearly 6%.
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